






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class 		SCREAMING_SNAKE_CASE__	(  __lowerCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  42



class 		SCREAMING_SNAKE_CASE__	(  __lowerCAmelCase					,      __lowerCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  True


  @register_to_config
  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			int = 3       ,				lowerCamelCase		:			int = 3       ,				lowerCamelCase		:			Tuple[str] = ("DownEncoderBlock2D",)       ,				lowerCamelCase		:			Tuple[str] = ("UpDecoderBlock2D",)       ,				lowerCamelCase		:			Tuple[int] = (64,)       ,				lowerCamelCase		:			int = 1       ,				lowerCamelCase		:			str = "silu"       ,				lowerCamelCase		:			int = 4       ,				lowerCamelCase		:			int = 32       ,				lowerCamelCase		:			int = 32       ,				lowerCamelCase		:			float = 0.1_8215       ,				)						->	int:


         """simple docstring"""
         super().__init__()

         # pass init params to Encoder
         _UpperCAmelCase							=    Encoder(
             in_channels=lowerCamelCase_       ,				out_channels=lowerCamelCase_       ,				down_block_types=lowerCamelCase_       ,				block_out_channels=lowerCamelCase_       ,				layers_per_block=lowerCamelCase_       ,				act_fn=lowerCamelCase_       ,				norm_num_groups=lowerCamelCase_       ,				double_z=lowerCamelCase_       ,				)

         # pass init params to Decoder
         _UpperCAmelCase							=    Decoder(
             in_channels=lowerCamelCase_       ,				out_channels=lowerCamelCase_       ,				up_block_types=lowerCamelCase_       ,				block_out_channels=lowerCamelCase_       ,				layers_per_block=lowerCamelCase_       ,				norm_num_groups=lowerCamelCase_       ,				act_fn=lowerCamelCase_       ,				)

         _UpperCAmelCase							=    nn.Convad(2 * latent_channels       ,				2 * latent_channels       ,				1							)
         _UpperCAmelCase							=    nn.Convad(lowerCamelCase_       ,				lowerCamelCase_       ,				1							)

         _UpperCAmelCase							=    False
         _UpperCAmelCase							=    False

         # only relevant if vae tiling is enabled
         _UpperCAmelCase							=    self.config.sample_size
         _UpperCAmelCase							=    (
             self.config.sample_size[0]
             if isinstance(self.config.sample_size       ,				(list, tuple)							)
             else self.config.sample_size
         )
         _UpperCAmelCase							=    int(sample_size / (2 ** (len(self.config.block_out_channels							) - 1))							)
         _UpperCAmelCase							=    0.25


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[int]=False							)						->	Union[str, Any]:


         """simple docstring"""


         if isinstance(lowerCamelCase_       ,				(Encoder, Decoder)							):
                _UpperCAmelCase							=    value


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			bool = True							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    use_tiling


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[Any]:


         """simple docstring"""
         self.enable_tiling(lowerCamelCase_							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    True


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    False


  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    {}

         def fn_recursive_add_processors(lowerCamelCase		:			str       ,				lowerCamelCase		:			torch.nn.Module       ,				lowerCamelCase		:			Dict[str, AttentionProcessor]							):
                if hasattr(lowerCamelCase_       ,				"""set_processor"""							):
                       _UpperCAmelCase							=    module.processor

                for sub_name, child in module.named_children():
                       fn_recursive_add_processors(f"""{name}.{sub_name}"""       ,				lowerCamelCase_       ,				lowerCamelCase_							)

                return processors

         for name, module in self.named_children():
                fn_recursive_add_processors(lowerCamelCase_       ,				lowerCamelCase_       ,				lowerCamelCase_							)

         return processors


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Union[AttentionProcessor, Dict[str, AttentionProcessor]]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    len(self.attn_processors.keys()							)

         if isinstance(lowerCamelCase_       ,				lowerCamelCase_							) and len(lowerCamelCase_							) != count:
                raise ValueError(
                    f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase_							)} does not match the"""
                    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

         def fn_recursive_attn_processor(lowerCamelCase		:			str       ,				lowerCamelCase		:			torch.nn.Module       ,				lowerCamelCase		:			Optional[Any]							):
                if hasattr(lowerCamelCase_       ,				"""set_processor"""							):
                       if not isinstance(lowerCamelCase_       ,				lowerCamelCase_							):
                              module.set_processor(lowerCamelCase_							)
                       else:
                              module.set_processor(processor.pop(f"""{name}.processor"""							)							)

                for sub_name, child in module.named_children():
                       fn_recursive_attn_processor(f"""{name}.{sub_name}"""       ,				lowerCamelCase_       ,				lowerCamelCase_							)

         for name, module in self.named_children():
                fn_recursive_attn_processor(lowerCamelCase_       ,				lowerCamelCase_       ,				lowerCamelCase_							)


  def  lowerCamelCase       (      self		:			Any							)						->	List[str]:


         """simple docstring"""
         self.set_attn_processor(AttnProcessor()							)


  @apply_forward_hook
  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			torch.FloatTensor       ,				lowerCamelCase		:			bool = True							)						->	Tuple:


         """simple docstring"""


         if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
                return self.tiled_encode(lowerCamelCase_       ,				return_dict=lowerCamelCase_							)

         if self.use_slicing and x.shape[0] > 1:
                _UpperCAmelCase							=    [self.encoder(lowerCamelCase_							) for x_slice in x.split(1							)]
                _UpperCAmelCase							=    torch.cat(lowerCamelCase_							)
         else:
                _UpperCAmelCase							=    self.encoder(lowerCamelCase_							)

         _UpperCAmelCase							=    self.quant_conv(lowerCamelCase_							)
         _UpperCAmelCase							=    DiagonalGaussianDistribution(lowerCamelCase_							)

         if not return_dict:
                return (posterior,)

         return AutoencoderKLOutput(latent_dist=lowerCamelCase_							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			torch.FloatTensor       ,				lowerCamelCase		:			bool = True							)						->	List[str]:


         """simple docstring"""


         if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
                return self.tiled_decode(lowerCamelCase_       ,				return_dict=lowerCamelCase_							)

         _UpperCAmelCase							=    self.post_quant_conv(lowerCamelCase_							)
         _UpperCAmelCase							=    self.decoder(lowerCamelCase_							)

         if not return_dict:
                return (dec,)

         return DecoderOutput(sample=lowerCamelCase_							)


  @apply_forward_hook
  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			torch.FloatTensor       ,				lowerCamelCase		:			bool = True							)						->	List[Any]:


         """simple docstring"""


         if self.use_slicing and z.shape[0] > 1:
                _UpperCAmelCase							=    [self._decode(lowerCamelCase_							).sample for z_slice in z.split(1							)]
                _UpperCAmelCase							=    torch.cat(lowerCamelCase_							)
         else:
                _UpperCAmelCase							=    self._decode(lowerCamelCase_							).sample

         if not return_dict:
                return (decoded,)

         return DecoderOutput(sample=lowerCamelCase_							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    min(a.shape[2]       ,				b.shape[2]       ,				lowerCamelCase_							)
         for y in range(lowerCamelCase_							):
                _UpperCAmelCase							=    a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    min(a.shape[3]       ,				b.shape[3]       ,				lowerCamelCase_							)
         for x in range(lowerCamelCase_							):
                _UpperCAmelCase							=    a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
         return b


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			torch.FloatTensor       ,				lowerCamelCase		:			bool = True							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)							)
         _UpperCAmelCase							=    int(self.tile_latent_min_size * self.tile_overlap_factor							)
         _UpperCAmelCase							=    self.tile_latent_min_size - blend_extent

         # Split the image into 512x512 tiles and encode them separately.
         _UpperCAmelCase							=    []
         for i in range(0       ,				x.shape[2]       ,				lowerCamelCase_							):
                _UpperCAmelCase							=    []
                for j in range(0       ,				x.shape[3]       ,				lowerCamelCase_							):
                       _UpperCAmelCase							=    x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                       _UpperCAmelCase							=    self.encoder(lowerCamelCase_							)
                       _UpperCAmelCase							=    self.quant_conv(lowerCamelCase_							)
                       row.append(lowerCamelCase_							)
                rows.append(lowerCamelCase_							)
         _UpperCAmelCase							=    []
         for i, row in enumerate(lowerCamelCase_							):
                _UpperCAmelCase							=    []
                for j, tile in enumerate(lowerCamelCase_							):
                       # blend the above tile and the left tile
                       # to the current tile and add the current tile to the result row
                       if i > 0:
                              _UpperCAmelCase							=    self.blend_v(rows[i - 1][j]       ,				lowerCamelCase_       ,				lowerCamelCase_							)
                       if j > 0:
                              _UpperCAmelCase							=    self.blend_h(row[j - 1]       ,				lowerCamelCase_       ,				lowerCamelCase_							)
                       result_row.append(tile[:, :, :row_limit, :row_limit]							)
                result_rows.append(torch.cat(lowerCamelCase_       ,				dim=3							)							)

         _UpperCAmelCase							=    torch.cat(lowerCamelCase_       ,				dim=2							)
         _UpperCAmelCase							=    DiagonalGaussianDistribution(lowerCamelCase_							)

         if not return_dict:
                return (posterior,)

         return AutoencoderKLOutput(latent_dist=lowerCamelCase_							)


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			torch.FloatTensor       ,				lowerCamelCase		:			bool = True							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)							)
         _UpperCAmelCase							=    int(self.tile_sample_min_size * self.tile_overlap_factor							)
         _UpperCAmelCase							=    self.tile_sample_min_size - blend_extent

         # Split z into overlapping 64x64 tiles and decode them separately.
         # The tiles have an overlap to avoid seams between tiles.
         _UpperCAmelCase							=    []
         for i in range(0       ,				z.shape[2]       ,				lowerCamelCase_							):
                _UpperCAmelCase							=    []
                for j in range(0       ,				z.shape[3]       ,				lowerCamelCase_							):
                       _UpperCAmelCase							=    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                       _UpperCAmelCase							=    self.post_quant_conv(lowerCamelCase_							)
                       _UpperCAmelCase							=    self.decoder(lowerCamelCase_							)
                       row.append(lowerCamelCase_							)
                rows.append(lowerCamelCase_							)
         _UpperCAmelCase							=    []
         for i, row in enumerate(lowerCamelCase_							):
                _UpperCAmelCase							=    []
                for j, tile in enumerate(lowerCamelCase_							):
                       # blend the above tile and the left tile
                       # to the current tile and add the current tile to the result row
                       if i > 0:
                              _UpperCAmelCase							=    self.blend_v(rows[i - 1][j]       ,				lowerCamelCase_       ,				lowerCamelCase_							)
                       if j > 0:
                              _UpperCAmelCase							=    self.blend_h(row[j - 1]       ,				lowerCamelCase_       ,				lowerCamelCase_							)
                       result_row.append(tile[:, :, :row_limit, :row_limit]							)
                result_rows.append(torch.cat(lowerCamelCase_       ,				dim=3							)							)

         _UpperCAmelCase							=    torch.cat(lowerCamelCase_       ,				dim=2							)
         if not return_dict:
                return (dec,)

         return DecoderOutput(sample=lowerCamelCase_							)





  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			torch.FloatTensor       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[torch.Generator] = None       ,				)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    sample
         _UpperCAmelCase							=    self.encode(lowerCamelCase_							).latent_dist
         if sample_posterior:
                _UpperCAmelCase							=    posterior.sample(generator=lowerCamelCase_							)
         else:
                _UpperCAmelCase							=    posterior.mode()
         _UpperCAmelCase							=    self.decode(lowerCamelCase_							).sample

         if not return_dict:
                return (dec,)

         return DecoderOutput(sample=lowerCamelCase_							)
import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _SCREAMING_SNAKE_CASE 		=      """0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if rng is None:
  UpperCamelCase			  =      random.Random()

 UpperCamelCase			  =      1
 for dim in shape:
  total_dims *= dim

 UpperCamelCase			  =      []
 for _ in range(UpperCamelCase_       ):
  values.append(rng.randint(0							,					vocab_size - 1       )       )

 UpperCamelCase			  =      np.array(UpperCamelCase_							,					dtype=jnp.intaa       ).reshape(UpperCamelCase_       )

 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ids_tensor(UpperCamelCase_							,					vocab_size=2							,					rng=UpperCamelCase_       )
 # make sure that at least one token is attended to for each batch
 UpperCamelCase			  =      1
 return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    # cut to half length & take max batch_size 3
    UpperCamelCase			  =      2
    UpperCamelCase			  =      inputs["""input_ids"""].shape[-1] // 2
    UpperCamelCase			  =      inputs["""input_ids"""][:max_batch_size, :sequence_length]

    UpperCamelCase			  =      jnp.ones_like(lowerCamelCase_				)
    UpperCamelCase			  =      attention_mask[:max_batch_size, :sequence_length]

    # generate max 5 tokens
    UpperCamelCase			  =      input_ids.shape[-1] + 5
    if config.eos_token_id is not None and config.pad_token_id is None:
     # hack to allow generate for models such as GPT2 as is done in `generate()`
     UpperCamelCase			  =      config.eos_token_id
    return config, input_ids, attention_mask, max_length







   @is_pt_flax_cross_test
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
     UpperCamelCase			  =      getattr(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      pt_model_class(lowerCamelCase_				).eval()
     UpperCamelCase			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,   flax_model.params				)

     UpperCamelCase			  =      flax_model.generate(lowerCamelCase_				).sequences
     UpperCamelCase			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,   dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      UpperCamelCase			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

     self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,   flax_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[0]   ,   input_ids.shape[0] * config.num_return_sequences				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0.8
    UpperCamelCase			  =      10
    UpperCamelCase			  =      0.3
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      2
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
    UpperCamelCase			  =      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

    UpperCamelCase			  =      """Hello world"""
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   return_tensors="""np"""				).input_ids

    # typos are quickly detected (the correct argument is `do_sample`)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """do_samples"""				):
     model.generate(lowerCamelCase_   ,   do_samples=lowerCamelCase_				)

    # arbitrary arguments that will not be used anywhere are also not accepted
    with self.assertRaisesRegex(lowerCamelCase_   ,   """foo"""				):
     UpperCamelCase			  =      {"""foo""": """bar"""}
     model.generate(lowerCamelCase_   ,   **lowerCamelCase_				)
